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1ACES scenarios workshop December 8, 2014
BACK TO THE FUTURE: SCENARIO GENERATOR FOR ECOSYSTEM SERVICES ANALYSIS
WWF and Natural Capital Project, ACES Workshop
December 8, 2014
Amy Rosenthal Nasser Olwero Nirmal Bhagabati
Adam Dixon Emily McKenzie Gregory Verutes
2ACES scenarios workshop December 8, 2014
SCENARIO GENERATORA new tool in the InVEST 3.1 software suite
3ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
• IDRISI Land Change Modeler
• Metronamica
• PoleStar
• IMAGE
• WaterGAP
• AIM
• GLOBIOM
• CLUE-S
• GTAP/MAGNET
• LandSHIFT
• International Futures Model
• Marxan
• Dinamica
• GeoMod
• Vensim
• MAGICC/SCENGEN
• IPAT Scenario Navigator
MANY OPTIONS, DIFFERENT STRENGTHS
4ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
• IDRISI Land Change Modeler
• Metronamica
• PoleStar
• IMAGE
• WaterGAP
• AIM
• GLOBIOM
• CLUE-S
• GTAP/MAGNET
• LandSHIFT
• International Futures Model
• Marxan
• Dinamica
• GeoMod
• Vensim
• MAGICC/SCENGEN
• IPAT Scenario Navigator
SPATIAL MODELS
5ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
WATER
• WaterGAP
CLIMATE
• MAGICC/SCENGEN
• AIM
• GTAP/MAGNET
• IMAGE
LAND USE PLANNING
• Metronamica
SYSTEM DYNAMICS
• Vensim
OPTIMIZATION
• Marxan
GLOBAL
• GLOBIOM
• IMAGE
• International Futures
SPECIFIC THEMES
6ACES scenarios workshop December 8, 2014
WHY ANOTHER TOOL?
• Complexity of modelling
• Lack of scenario development expertise
• Data scarcity
• Time required
• Translating qualitative to quantitative
• Engaging and using stakeholder input
CURRENT CHALLENGES IN PRACTICE
@ Taylor Ricketts
7ACES scenarios workshop December 8, 2014
WHY ANOTHER TOOL?
Experience in Tanzania• Sparse data
• Stakeholder engagement
• Few easy tools
• Many steps in GIS
• Difficult to estimate relative strength of drivers
SCENARIO GENERATOR
Swetnam et al. 2011
8ACES scenarios workshop December 8, 2014
INVEST SCENARIO GENERATORConverting storylines into maps
9ACES scenarios workshop December 8, 2014
• Primarily designed to incorporate – Storylines based on stakeholder input, often gathered in a workshop setting
– Inputs gleaned from scientific literature surveys, policy documents, etc.
• Converts these inputs into a transition likelihood that a given pixel will change to a different land cover in the future
INVEST SCENARIO GENERATOR
10ACES scenarios workshop December 8, 2014
INVEST SCENARIO GENERATOR
• Econometric modeling tool• Forecasting model• Optimization tool• Regression-based• Highly complex
WHAT IT’S NOT
11ACES scenarios workshop December 8, 2014
ILLEGAL GOLD MINING, PERUA CASE FROM SCENARIO GENERATOR
12ACES scenarios workshop December 8, 2014
ILLEGAL GOLD MINING, PERUAN EXAMPLE
13ACES scenarios workshop December 8, 2014
ILLEGAL GOLD MINING, PERUAN EXAMPLE
14ACES scenarios workshop December 8, 2014
SCENARIOHUB.NET
Web-based forms to gather info from stakeholders & experts
Info converted to inputs in correct format for Scenario Generator
Under construction…
15ACES scenarios workshop December 8, 2014
GLOSSARY
• Drivers• Storylines• Transition likelihood• Factors• Patch size• Constraints• Overrides
TERMS IN SCENARIO GENERATOR
16ACES scenarios workshop December 8, 2014
DRIVERS
17ACES scenarios workshop December 8, 2014
STORYLINES
From: McKenzie, E., A. Rosenthal et al. 2012. Developing scenarios to assess ecosystem service tradeoffs: Guidance and case studies for InVEST users. World Wildlife Fund, Washington, D.C
18ACES scenarios workshop December 8, 2014
SPATIAL RULESIN SCENARIO GENERATOR
19ACES scenarios workshop December 8, 2014
TRANSITION LIKELIHOODGOING FROM STORYLINES TO MAPS
Forest AgricultureGrassland
Built
72
13
1
Original
Forest AgricultureGrassland BuiltScenario
20ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
LocationGA
IN
LOSS
21ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
Quantity
22ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
Quantity
Note: in the current version of the tool, you can only specify increases.
Decreases will be addressed in a future version
X
23ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
24ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
25ACES scenarios workshop December 8, 2014
TRANSITION MATRIX
Fore
st
Gra
ssla
nd
Agr
icul
ture
Urb
an
Cha
nge
Prox
imity
Prox
imity
di
stan
ce
Prio
rity
/Forest 0 1 7 2 -30% 0 0 0
Grassland 0 0 3 1 -40% 0 0 0
Agriculture 0 0 0 0 50 1 10 2
Urban 0 0 0 0 10 1 5 1
26ACES scenarios workshop December 8, 2014
FACTORS
• In the scenario tool, “factors" are rules that increase or reduce likelihood of a change in land cover
• E.g., – Deforestation may be higher close to roads and
cities
– Agriculture may occur only within certain ranges of elevation or slope
27ACES scenarios workshop December 8, 2014
CONSTRAINTS
• Areas where a change cannot take place, or has a lower propensity to take place
• E.g., no-go or limited conversion zones, enforced protected areas
28ACES scenarios workshop December 8, 2014
PATCH SIZE
• Minimum area threshold for a land cover or use
• E.g., new large-scale agricultural areas have to be at least 10 hectares
© Saudi Arabia Investment Group
29ACES scenarios workshop December 8, 2014
OVERRIDES
• Areas where a land cover change will definitely take place
• E.g., a forest concession destined to convert to an oil palm plantation under BAU
30ACES scenarios workshop December 8, 2014
MODEL FLOW
Current land cover
Factors
Transition matrix
Suitability
Patch size
Allocation
Constraints Proximity
Override Scenario
RulesRules
31ACES scenarios workshop December 8, 2014
SCENARIOS QUIZWhat have you learned?
32ACES scenarios workshop December 8, 2014
CONSIDERATIONS
• Accuracy depends on stakeholders
• Stakeholder-given values are best for near future
• Currently, users can input desired increase in a specific land cover, but not loss– This feature will be added in future releases
• Model assumes a cover type either increases or decreases but not both
• Assumes a single-step transition
CURRENT LIMITATIONS
33ACES scenarios workshop December 8, 2014
USER NOTES
• Minimize number of transitions, select most important ones
• Iterative process
34ACES scenarios workshop December 8, 2014
SCENARIOHUB.NET
Web-based forms to gather info from stakeholders & experts
Info converted to inputs in correct format for Scenario Generator
Under construction…
35ACES scenarios workshop December 8, 2014
DEMO
36ACES scenarios workshop December 8, 2014
LET’S RUN IT!Scenario Generator
37ACES scenarios workshop December 8, 2014
SCENARIO GENERATORINVEST 3.1.0
38ACES scenarios workshop December 8, 2014
SCENARIO GENERATORINVEST 3.1.0
39ACES scenarios workshop December 8, 2014
SCENARIO GENERATORINVEST 3.1.0
40ACES scenarios workshop December 8, 2014
41ACES scenarios workshop December 8, 2014
SAMPLE DATAGREATER VIRUNGA REGION, EAST AFRICA
42ACES scenarios workshop December 8, 2014
VIRUNGAS EXAMPLE
43ACES scenarios workshop December 8, 2014
TANINTHARYI, MYANMARScenario Generator case
44ACES scenarios workshop December 8, 2014
• Biodiverse forests
• Infrastructure development
• Complex politics around land use, reform, resettlement
• Lessons for national-level planning
45ACES scenarios workshop December 8, 2014
SCENARIO ASSUMPTIONS
46ACES scenarios workshop December 8, 2014
CONSTRAINTS & OVERRIDES
Constraints Overrides
47ACES scenarios workshop December 8, 2014
ADDITIONAL INPUTS
Footprint of reservoir due to proposed damOverride
Factor: roads
48ACES scenarios workshop December 8, 2014
BASELINE & SCENARIOS
49ACES scenarios workshop December 8, 2014
Baseline land cover (2013)
50ACES scenarios workshop December 8, 2014
“Limited conversion” scenario
51ACES scenarios workshop December 8, 2014
“More conversion” scenario
Dam reservoir
Mangrove loss
52ACES scenarios workshop December 8, 20141/6/2015
Baseline
2013 land cover
53ACES scenarios workshop December 8, 20141/6/2015
“Limited conversion”
Some deforestation around roads and population centers
54ACES scenarios workshop December 8, 20141/6/2015
“More conversion”
More deforestation around roads and population centers
55ACES scenarios workshop December 8, 2014
ECOSYSTEM SERVICE OUTCOMES UNDER TANINTHARYI SCENARIOS
56ACES scenarios workshop December 8, 2014
CARBON EMISSIONS
57ACES scenarios workshop December 8, 2014
EROSION
58ACES scenarios workshop December 8, 2014
59ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR
• Turn storylines into maps
• Useful for projects with short timeline or in data-sparse environment
• Translation of qualitative & stakeholder inputs into scenario maps
• Replicable
• Good ‘what-if’ tool
• Particularly good for visions, explorations & interventions (in data-poor areas)
WHAT IT DOES
60ACES scenarios workshop December 8, 2014
SCENARIO MODELING TOOLS
• Land Change Modeler
• CLUE
• LandSHIFT
• Marxan
• Dinamica
• GeoMod
• MAGICC/SCENGEN
• Metronamica
SPATIAL MODELS
61ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR KITWHAT IT DOES
62ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR KIT
Scenario Documenter
• Can be used in workshop setting with stakeholders
• Inputs from scientific literature, surveys & policy documents
Scenario Generator
• Converts inputs into transition likelihood that a given pixel will change to a different land cover in the future
• Based on total area allocated, user-defined constraints, proximity and access rules
Survey for Scenarios
• Gathers info from stakeholders & experts
• Info converted to kit-ready inputs for Scenario Hub and Generator
• (Under construction!)
WHAT IT DOES
63ACES scenarios workshop December 8, 2014
Likelihood(+)Factors(+)Proximity (+)Constraints (x)
weighted
Aggregate transition probability
Rules
Scenario Documenter
Scenario Generator
Survey for Scenarios
64ACES scenarios workshop December 8, 2014
SCENARIO GENERATOR
• Econometric modeling tool
• Projection or prediction model
• Optimization tool
• Regression-based
• Highly complex
WHAT IT’S NOT
65ACES scenarios workshop December 8, 2014
SCHEMATIC
Allocation
Transition rate Priority
Override
Scenario
Quantity What goes first?
66ACES scenarios workshop December 8, 2014
ALLOCATION
Suitability
Val (n) = 0 – 100Start from n = 100
Goal met?
Go to next cover
Yes
Suitable cells < goal?
No
Yes
Convert cells to Ag
y
Go to next lower
suitability
Suitable cells > 0?
No Group and select
Yes
No
Cells groupedTo minimum Patch size and selectedrandomly
In order ofpriority
(n = n-1)
While n > 0
No
67ACES scenarios workshop December 8, 2014
PREPARING SUITABILITY LAYERS
Likelihood(+)
Factors(+)
Proximity (+)
Constraints (x)
weighted
Aggregate transition probability/suitability
Rules